#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# -*- coding: utf-8 -*-

import parl
from parl import layers  # 封装了 paddle.fluid.layers 的API


class Model_2048(parl.Model):
    def __init__(self, act_dim):
        # hid1_size = 128
        # hid2_size = 128
        # # 3层全连接网络
        # self.fc1 = layers.fc(size=hid1_size, act='relu')
        # self.fc2 = layers.fc(size=hid2_size, act='relu')
        # self.fc3 = layers.fc(size=act_dim, act=None)

        self.c1 = layers.conv2d(num_filters=128, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c2 = layers.conv2d(num_filters=128, filter_size=(1, 2), act="relu", bias_attr=True)
        self.c11 = layers.conv2d(num_filters=128, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c12 = layers.conv2d(num_filters=128, filter_size=(1, 2), act="relu", bias_attr=True)
        self.c21 = layers.conv2d(num_filters=128, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c22 = layers.conv2d(num_filters=128, filter_size=(1, 2), act="relu", bias_attr=True)

        # self.h = layers.flatten(axis=1, name=None)

        # hidden = layers.concat(input=[self.h1, self.h2, self.h11, self.h12, self.h21, self.h22], axis=0)

        self.fc1 = layers.fc(size=512, act='relu')
        # self.fc2 = layers.fc(size=125, act='relu')
        # self.fc3 = layers.fc(size=64, act='relu')
        # self.fc4 = layers.fc(size=64, act='relu')
        self.fc5 = layers.fc(size=act_dim, act='relu')

    def value(self, obs):
        r1 = layers.flatten(self.c1(obs), axis=1)  # axis?
        r2 = layers.flatten(self.c2(obs), axis=1)
        # r11 = layers.flatten(self.c1(self.c1(obs)), axis=1)
        # r12 = layers.flatten(self.c2(self.c1(obs)), axis=1)
        # r21 = layers.flatten(self.c1(self.c2(obs)), axis=1)
        # r22 = layers.flatten(self.c2(self.c2(obs)), axis=1)
        r11 = layers.flatten(self.c11(self.c1(obs)), axis=1)
        r12 = layers.flatten(self.c12(self.c1(obs)), axis=1)
        r21 = layers.flatten(self.c21(self.c2(obs)), axis=1)
        r22 = layers.flatten(self.c22(self.c2(obs)), axis=1)

        # print(r1.shape)
        # print(r11.shape)
        # print(r12.shape)
        # print(r21.shape)
        # print(r22.shape)

        hidden = layers.concat(input=[r1, r2, r11, r12, r21, r22], axis=1)  # axis?

        # q = self.fc5(self.fc4(self.fc3(self.fc2(self.fc1(hidden)))))
        q = self.fc5(self.fc1(hidden))
        return q
